Technical Field
The present invention relates to estimating a characteristic of a posterior distribution for a plurality of samples.
Related Art
Estimating values of a plurality of variables is important in interactive cognitive systems. For example, a user's likelihood of preferring certain features of a product, such as price, functionality, durability, etc., can be estimated. Such user preferences are typically represented by high-dimensional vectors, such as feature vectors, and can be represented with a probability (posterior) distribution on a metric space. Some characteristics, such as differential entropy, of the probability distribution can be used to measure how reliably a user's preference has been predicted.
However, analytical solutions for estimating the characteristics of the posterior distribution have been limited. One approach is to generate samples from the posterior distribution by the use of Markov Chain Monte Carlo (MCMC) algorithms for every possible observation, and estimate the differential entropy from those samples. However, repetitive MCMC generation of samples for each hypothetical observation and computation of differential entropy values under this approach is often time consuming, and is computationally expensive.